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生态和环境管理方面的很多基本问题都需要涉及土壤及腐殖质小型动物多样性的特征描述.当前利用高通量测序技术获得条形码基因扩增子序列的方法(’metabarcoding’)在生物多样性调查方面提供了高效有力的方法.然而,这一技术的广泛应用面临很大阻碍,即需要从大量原始序列数据中通过生物信息学方法处理获得很多候选基因.于是,我们比较了3个针对从固体基质中获得的18S rDNA metabarcode数据的信息学处理方法:(ⅰ)USEARCH/CROP,(ⅱ)Denoiser/UCLUST以及(ⅲ)OCTUPUS.令人满意的是,这3个信息学处理方法得到了相似且与环境样本中已知特征分类单元高度相关的群落组成.然而,OCTUPUS由于过高的序列噪音出现了过度估计系统发育多样性的问题.因此,推荐USEARCH/CROP或Denoiser/UCLUST方法,二者均可在QIIME环境下运行.
Many of the basic issues in ecology and environmental management require characterization of the diversity of soil and humus miniature animals.Currently, methods for obtaining barcode gene amplicon sequences using high-throughput sequencing (’metabarcoding’) have been used in biodiversity surveys Provides a powerful and efficient method.However, the widespread use of this technology faces a major hurdle, which requires a large number of raw sequence data obtained by bioinformatics approach to obtain a lot of candidate genes.Then, we compared three from the solid matrix (I) USEARCH / CROP, (ii) Denoiser / UCLUST, and (iii) OCTUPUS. Satisfactorily, the three informatics processing approaches are similar and correspond to However, OCTUPUS suffers from excessive overestimation of phylogenetic diversity due to excessive sequence noise, so the USEARCH / CROP or Denoiser / UCLUST methods are recommended, either Run in QIIME environment.